“…We developed two sets of case-identification algorithms: one based on a literature review and clinical input (predefined algorithms), and another using machine learning (ML) methods (logistic regression, classification and regression tree, random forest). Patient classifications based on these algorithms were compared and validated against the chart data. …”
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“…A structured questionnaire survey was conducted via face-to-face interviews. Random forest and logistic regression analysis were used to demonstrate the effects of the explanatory variables on health seeking behaviors from predisposing, enabling and need variables. …”
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“…To illustrate these performance metrics, different modeling approaches for predicting treatment effect are applied to the data of the Diabetes Prevention Program: 1) a risk modelling approach with restricted cubic splines; 2) an effect modelling approach including penalized treatment interactions; and 3) the causal forest. RESULTS: As desired, performance metric values of “perturbed models” were consistently worse than those of the “optimal model” (E(avg)-for-benefit ≥ 0.043 versus 0.002, E(50)-for-benefit ≥ 0.032 versus 0.001, E(90)-for-benefit ≥ 0.084 versus 0.004, cross-entropy-for-benefit ≥ 0.765 versus 0.750, Brier-for-benefit ≥ 0.220 versus 0.218). …”
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“…RESULTS: Balanced random forest classifiers were successful at predicting well-being outcomes, with accuracies ranging between 78% and 82% (for 12-month end points after diagnosis) and between 74% and 83% (for 18-month end points after diagnosis). …”
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“…RESULTS: We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). …”
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“…The predictive models were developed using eight ML algorithms including the J48 decision tree (J48), support vector machine (SVM), multi-layer perceptron (MLP), k-nearest neighbourhood (k-NN), Naïve Bayes (NB), logistic regression (LR), random forest (RF), and eXtreme gradient boosting (XGBoost). …”
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“…METHODS: Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. …”
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“…The score provided complementary prognostic information beyond conventional risk factors (C index: 0.78 vs 0.81; overall net reclassification improvement index [95% confidence interval]: 0.255 [0.204–0.299]; likelihood ratio test P < 0.001), and was identified as the most important predictor of mortality by the proportion of explainable log-likelihood ratio χ(2) statistics, the best subset analysis, as well as the random survival forest analysis in most types of VHD. The predictive performance of the score was also demonstrated in patients under conservative treatment, with normal left ventricular systolic function, or with primary VHD. …”
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“…When ICG-R15 = 20%, classifier Random Forest performed best with AUC = 0.979 and ACC = 0.882. …”
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“…After polyploidization, the lignin content of forest trees is generally reduced, which is considered a beneficial genetic improvement. …”
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“…Univariate COX and random forest analyses were used to screen prognosis-associated genes and construct models. …”
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“…Statistical analyses and forest plots were generated using Review Manager and STATA software. …”
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“…Furthermore, we drew a forest plot for each outcome. We conducted a sensitivity analysis, data analysis, heterogeneity, and publication bias test. …”
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“…We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R(2): 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m(3) for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. …”
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“…Next, a Random Survival Forest (RSF) was carried out to detect the lipidic isomers with the lowest prediction error, these lipids were then used to build a Lipidomic Risk (LR) score which was evaluated through a Cox. …”
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“…PM(2.5) and five constituents were estimated by satellite-based random forest models. Dietary approaches to stop hypertension (DASH) and alternative Mediterranean diet (AMED) scores were calculated for each participant. …”
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“…Factors which significantly predicted early discharge in multivariable regression were: maternal age < 20 years (compared to 20–29 years, aOR: 1.44; 95%CI 1.13–1.82), unemployment (aOR: 0.78; 95%CI: 0.63–0.96), non-Christian religions (aOR: 1.65; 95CI: 1.21–2.24), and region of residence—Northern zone aOR:9.95 (95%CI:6.53–15.17) and Forest zone aOR:2.51 (95%CI:1.79–3.53) compared to the country’s capital cities (Douala or Yaounde). …”
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“…METHODS: We conducted a qualitative study in Kitale, Webuye, Kocholya, Turbo, Mosoriot and Burnt Forest areas of Western Kenya. We utilized the PRECEDE-PROCEED framework to understand the behavioral, environmental and ecological factors that would influence uptake and success of our intervention. …”
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“…The characteristics of the key OSPM genes were summarized in pan-cancer. The random survival forest analysis and multivariate Cox regression analysis were utilized to construct an OSPM-related signature. …”
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“…For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. …”
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